Examples of org.apache.mahout.classifier.ResultAnalyzer.addInstance()

Class org.apache.mahout.classifier.ResultAnalyzer

Examples of org.apache.mahout.classifier.ResultAnalyzer.addInstance()

org.apache.mahout.classifier.ResultAnalyzer.addInstance()
@param correctLabel The correct label @param classifiedResult The classified result @return whether the instance was correct or not

      assertEquals(3, classifier.classifyDocument(document.toArray(new String[document.size()]),
        params.get("defaultCat"), 100).length);
      ClassifierResult result = classifier.classifyDocument(document.toArray(new String[document.size()]), params
          .get("defaultCat"));
      assertEquals(entry[0], result.getLabel());
      resultAnalyzer.addInstance(entry[0], result);
    }
    int[][] matrix = resultAnalyzer.getConfusionMatrix().getConfusionMatrix();
    for (int i = 0; i < 3; i++) {
      for (int j = 0; j < 3; j++) {
        assertEquals(i == j ? 4 : 0, matrix[i][j]);

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            for (Map.Entry<String, List<String>> stringListEntry : document.entrySet()) {
              List<String> strings = stringListEntry.getValue();
              ClassifierResult classifiedLabel = classifier.classify(model,
                  strings.toArray(new String[strings.size()]),
                  defaultCat);
              resultAnalyzer.addInstance(correctLabel, classifiedLabel);
            }
          }
          log.info("{}\t{}\t{}/{}", new Object[]{
              correctLabel,
              resultAnalyzer.getConfusionMatrix().getAccuracy(correctLabel),

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          regressionAnalyzer.setInstances(results);
          log.info("{}", regressionAnalyzer);
        } else {
          ResultAnalyzer analyzer = new ResultAnalyzer(Arrays.asList(dataset.labels()), "unknown");
          for (double[] res : results) {
            analyzer.addInstance(dataset.getLabelString(res[0]),
              new ClassifierResult(dataset.getLabelString(res[1]), 1.0));
          }
          log.info("{}", analyzer);
        }
      }

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        regressionAnalyzer.setInstances(resList.toArray(results));
        log.info("{}", regressionAnalyzer);
      } else {
        ResultAnalyzer analyzer = new ResultAnalyzer(Arrays.asList(dataset.labels()), "unknown");
        for (double[] r : resList) {
          analyzer.addInstance(dataset.getLabelString(r[0]),
            new ClassifierResult(dataset.getLabelString(r[1]), 1.0));
        }
        log.info("{}", analyzer);
      }
    }

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      Vector result = classifier.classifyFull(input);
      int cat = result.maxValueIndex();
      double score = result.maxValue();
      double ll = classifier.logLikelihood(actual, input);
      ClassifierResult cr = new ClassifierResult(newsGroups.values().get(cat), score, ll);
      ra.addInstance(newsGroups.values().get(actual), cr);


    }
    output.println(ra);
  }

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      Vector result = classifier.classifyFull(next.getSecond().get());
      int cat = result.maxValueIndex();
      double score = result.maxValue();
      double ll = classifier.logLikelihood(actual, next.getSecond().get());
      ClassifierResult cr = new ClassifierResult(asfDictionary.values().get(cat), score, ll);
      ra.addInstance(asfDictionary.values().get(actual), cr);


    }
    output.println(ra);
  }

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            ClassifierResult classifiedLabel = classifier.classifyDocument(
                strings.toArray(new String[strings.size()]), params
                    .get("defaultCat"));
            call.end();
            outercall.end();
            boolean correct = resultAnalyzer.addInstance(correctLabel,
                classifiedLabel);
            if (verbose) {
              // We have one document per line
              log.info("Line Number: " + lineNum + " Line(30): "
                  + (line.length() > 30 ? line.substring(0, 30) : line)

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      assertEquals(3, classifier.classifyDocument(document.toArray(new String[document.size()]),
        params.get("defaultCat"), 100).length);
      ClassifierResult result = classifier.classifyDocument(document.toArray(new String[document.size()]), params
          .get("defaultCat"));
      assertEquals(entry[0], result.getLabel());
      resultAnalyzer.addInstance(entry[0], result);
    }
    int[][] matrix = resultAnalyzer.getConfusionMatrix().getConfusionMatrix();
    for (int i = 0; i < 3; i++) {
      for (int j = 0; j < 3; j++) {
        assertEquals(i == j ? 4 : 0, matrix[i][j]);

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      assertEquals(3, classifier.classifyDocument(document.toArray(new String[document.size()]),
        params.get("defaultCat"), 100).length);
      ClassifierResult result = classifier.classifyDocument(document.toArray(new String[document.size()]), params
          .get("defaultCat"));
      assertEquals(entry[0], result.getLabel());
      resultAnalyzer.addInstance(entry[0], result);
    }
    int[][] matrix = resultAnalyzer.getConfusionMatrix().getConfusionMatrix();
    for (int i = 0; i < 3; i++) {
      for (int j = 0; j < 3; j++) {
        assertEquals(i == j ? 4 : 0, matrix[i][j]);

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            TimingStatistics.Call outercall = totalStatistics.newCall();
            ClassifierResult classifiedLabel = classifier.classifyDocument(strings.toArray(new String[strings
                .size()]), params.get("defaultCat"));
            call.end();
            outercall.end();
            boolean correct = resultAnalyzer.addInstance(correctLabel, classifiedLabel);
            if (verbose) {
              // We have one document per line
              log.info("Line Number: {} Line(30): {} Expected Label: {} Classified Label: {} Correct: {}",
                new Object[] {lineNum, line.length() > 30 ? line.substring(0, 30) : line, correctLabel,
                              classifiedLabel.getLabel(), correct,});

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